v12-dfs

admin_issue_key

Issue Key

Idempotently issue a user's API key and ALWAYS return the paste-able connect block. Returns the email's existing active key (or mints one) so a user keeps a single key across dashboard reloads; with rotate=true, revokes the existing keys and mints a fresh one. Admin-gated — the dashboard's per-user provision route (which verifies the user's Firebase token) calls this server-side, never the browser.

Responses:

200: Successful Response (Success Response) Content-Type: application/json

Example Response:

{
  "api_key": "Api Key",
  "tier": "Tier",
  "limits": {}
}

422: Validation Error Content-Type: application/json

Example Response:

{
  "detail": [
    {
      "loc": [],
      "msg": "Message",
      "type": "Error Type",
      "ctx": {}
    }
  ]
}

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tier`	No		personal
`email`	Yes
`rotate`	No	Revoke existing keys and mint a fresh one.

TDQS

A4.1/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully covers behavior: idempotent key issuing, reuse of existing keys, optional rotation that revokes and mints new keys, and admin gating. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured with sections, but includes example responses which add length. Generally concise given the complexity, though could be slightly trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description includes response format and examples, covering main points. Lacks details on error conditions beyond validation, but adequate for the tool type.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 33%, only 'rotate' has a description. Description adds context for rotate behavior but does not explain 'tier' values beyond the regex pattern. It adds some value but insufficiently compensates for low coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it issues API keys idempotently and returns a connect block. It distinguishes itself from sibling tools (mostly betting-related) as an admin function. The verb 'issue' and resource 'API key' are specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context that it is admin-gated and used server-side, but does not explicitly state when to use vs. alternatives or when not to use it. Given siblings are unrelated, a 3 is appropriate for implied usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.8/5.0

Disambiguation3/5

The betting_* cluster is clearly namespaced, but betting_best_bets and betting_scan_edges both return a ranked board of top prop edges, and betting_market overlaps with betting_game_lines and betting_sharp/cross_book in purpose. The detailed descriptions reduce misselection, but several boundaries are not crisp.

Naming Consistency3/5

The set is uniformly snake_case with helpful cluster prefixes like betting_, list_, and slate_, so it reads predictably. However, it mixes verb_noun names (generate_lineups, run_mlb_postmortem), noun phrases (betting_market, health_check/health_v12), and adjective-noun names (betting_best_bets, betting_sharp), so there isn't one consistent pattern.

Tool Count2/5

34 tools is above the 25+ threshold for a single MCP server, even considering the combined betting/DFS/contest scope. Several tools could be consolidated — betting_best_bets vs betting_scan_edges, health_check vs health_v12, and the two guides — making the surface feel heavy rather than lean.

Completeness5/5

The betting lifecycle is covered end-to-end: raw markets, models, edge scans, value checks, parlay building, bet logging/settlement, and P&L/CLV. The DFS side covers slates, player pools, lineup generation/fill, presets, contests, diff/health/refresh, and postmortems, leaving no obvious dead end for agents.

Resources

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